The conventional SQA system consists of the cascade of an Automatic Speech Recognition (ASR) engine followed by a text-trained QA (TQA) model. However, speech recognition errors naturally cause catastrophic problems to the TQA. [1]}, and several works [2]}, [3]}, [4]}, [5]} intended to alleviate such problems by knowledge distillation, including adapting the TQA model to be more robust against recognition errors. Some other efforts [6]}, [7]} exploited paired speech and transcripts to construct a cross-modal speech and text pre-trained model with aligned semantics fine-tuned end-to-end, in which the speech recognition error problems can be mitigated to some degree, and SQA performance improved.
